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Answer: Use the 'databricks jobs create' command to create the job, and then use the 'databricks jobs run-now' command to deploy it. Monitor its progress using the 'databricks jobs list-runs' command. This option provides a complete solution from job creation to deployment and monitoring.
Option C is the correct approach because it follows the proper sequence of creating the job with 'databricks jobs create', deploying it using 'databricks jobs run-now', and monitoring its progress with 'databricks jobs list-runs'. This method ensures that the job is properly set up before deployment and allows for effective monitoring, aligning with Databricks CLI best practices. The other options either miss critical steps or use incorrect commands, making them less suitable for the given scenario.
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In your role as a Databricks Certified Data Engineer - Professional, you are tasked with deploying and monitoring a Databricks job using the CLI. The job is critical for processing nightly data loads and must be monitored closely to ensure timely completion. Given the constraints of cost efficiency and the need for real-time monitoring, which of the following sequences of commands would you use to first deploy the job and then monitor its progress effectively? Choose the best option that aligns with Databricks CLI best practices.
A
Use the 'databricks jobs run-now' command to deploy the job, and then use the 'databricks jobs list-runs' command to monitor its progress. This approach is straightforward but lacks the initial job creation step.
B
Use the 'databricks jobs submit' command to deploy the job, and then use the 'databricks jobs get-run' command to monitor its progress. This option skips the job creation and directly submits the job, which may not be suitable for all scenarios.
C
Use the 'databricks jobs create' command to create the job, and then use the 'databricks jobs run-now' command to deploy it. Monitor its progress using the 'databricks jobs list-runs' command. This option provides a complete solution from job creation to deployment and monitoring.
D
Use the 'databricks jobs deploy' command to deploy the job, and then use the 'databricks jobs monitor' command to track its progress. This option uses non-existent commands, making it incorrect.